Large-Scale Learning with Fourier Features and Tensor Decompositions. - Frederiek Wesel; Kim Batselier (NeurIPS, 2021)

Towards Green AI with tensor networks - Sustainability and innovation enabled by efficient algorithms. - Eva Memmel; Clara Menzen; Jetze Schuurmans; Frederiek Wesel; Kim Batselier (CoRR, 2022)

Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data. - Frederiek Wesel; Kim Batselier (AISTATS, 2023)

Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models. - Frederiek Wesel; Kim Batselier (AISTATS, 2024)

Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine. - Seline J. S. de Rooij; Frederiek Wesel; Borbála Hunyadi (EUSIPCO, 2024)

Position: Tensor Networks are a Valuable Asset for Green AI. - Eva Memmel; Clara Menzen; Jetze Schuurmans; Frederiek Wesel; Kim Batselier (ICML, 2024)

A Kernelizable Primal-Dual Formulation of the Multilinear Singular Value Decomposition. - Frederiek Wesel; Kim Batselier (CoRR, 2024)

Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices. - Frida Marie Viset; Anton Kullberg; Frederiek Wesel; Arno Solin (Trans. Mach. Learn. Res., 2024)

Tensor Network-Constrained Kernel Machines as Gaussian Processes. - Frederiek Wesel; Kim Batselier (AISTATS, 2025)